Search

This document summarizes a study on the gastrointestinal side effects of a combination therapy for type 2 diabetes. The study found that when a glucose-dependent insulinotropic polypeptide receptor agonist (GIPRA) was used in conjunction with a glucagon-like peptide-1 receptor agonist (GLP-1 RA), there were fewer gastrointestinal adverse events compared to using the GLP-1 RA alone. The study suggests that the GIPRA component of the therapy may improve the tolerability of GLP-1 RA treatment, potentially leading to better effectiveness and adherence. The study acknowledges limitations such as a small number of female participants and limited ethnic diversity. [Extracted from the article]

In this study, a computer-driven, phoneme-agnostic method was explored for assessing speech disorders (SDs) in children, bypassing traditional labor-intensive phonetic transcription. Using the SpeechMark® automatic syllabic cluster (SC) analysis, which detects sequences of acoustic features that characterize well-formed syllables, 1952 American English utterances of 60 preschoolers were analyzed [16 with speech disorder present (SD-P) and 44 with speech disorder not present (SD-NP)] from two dialectal areas. A four-factor regression analysis evaluated the robustness of seven automated measures produced by SpeechMark® and their interactions. SCs significantly predicted SD status (p < 0.001). A secondary analysis using a generalized linear model with a negative binomial distribution evaluated the number of SCs produced by the groups. Results highlighted that children with SD-P produced fewer well-formed clusters [incidence rate ratio (IRR) = 0.8116, p ≤ 0.0137]. The interaction between speech group and age indicated that the effect of age on syllable count was more pronounced in children with SD-P (IRR = 1.0451, p = 0.0251), suggesting that even small changes in age can have a significant effect on SCs. In conclusion, speech status significantly influences the degree to which preschool children produce acoustically well-formed SCs, suggesting the potential for SCs to be speech biomarkers for SD in preschoolers. [ABSTRACT FROM AUTHOR]

In this paper, we derive several fuzzy differential subordination and fuzzy differential superordination results for analytic functions Ms,γξ,β, which involve the extended Mittag-Leffler function and the Pascal distribution series. We also investigate and introduce a class MBF,s,γξ,β (ρ) of analytic and univalent functions in the open unit disc D by employing the newly defined operator Ms,γξ,β. We determine a specific relationship of inclusion for this class. Further, we establish prerequisites for a function role in serving as both the fuzzy dominant and fuzzy subordinant of the fuzzy differential subordination and superordination, respectively. Some novel results that are sandwich-type can be found here. [ABSTRACT FROM AUTHOR]

I simplify and note extensions of results of Shibu et al. Sankhy a ¯ , Ser. A85 (2023a,b) concerning integral representations involving the probability generating function for inverse moments of positive discrete random variables, both univariate and multivariate. [ABSTRACT FROM AUTHOR]

Cystic Echinococcosis is a parasitic disease caused by the larvae of Echinococcus granulosus. The transmission of E. granulosus is affected by environmental changes and anthropogenic factors, which are in turn influenced by changes in the spatial and population dynamics of animals. The deterministic model can be extended stochastically to address the low prevalence rate, which is often observed in small mammal host populations, and to account for complex processes that reflect the highly widespread disease reservoirs and non-random mixing, such as the heterogeneous contact patterns of susceptible hosts with infectious materials. In this study, a mathematical model based on a set of differential equations that define the continuous transition between different classes was used is not regulated by host. The findings indicate that each protoscolex has an equal chance of developing into a worm and creating a dispersed population. Empirical modeling can be used to represent the frequency distribution of the number of parasites in each host using the negative binomial distribution. [ABSTRACT FROM AUTHOR]

This research presents a model called the 'Gaussian negative binomial mixture with a latent class choice model', which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes. [ABSTRACT FROM AUTHOR]

This document provides a list of references and citations from scientific articles and books related to topics such as ecology, microbiology, bioinformatics, and statistical analysis. The references cover a range of research methods and findings in these fields, making it a valuable resource for library patrons conducting research in these areas. [Extracted from the article]

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias. [ABSTRACT FROM AUTHOR]

Objectives: Screening blood units for compatibility constitutes a Bernoulli series. Estimating the number of units needed to be screened represents a classic waiting time problem that may be resolved using the Negative Binomial Distribution. The currently recommended method for estimating the number of units screened, n, to find a required number of compatible units, r, with a given probability, p, is n = r/p. This coincides with the mean of the Negative Binomial Distribution so that the actual number of units screened will often be underestimated by the current method. Methods: The cumulative distribution function of the Negative Binomial Distribution provides the probability of success (compatibility), F(n;r,p), as a function of the number of trials performed (attempted crossmatches), n, the probability of success on each trial, p, and the number of successes (compatible units) required, r. Choosing a threshold cumulative probability sufficiently high, such as F ~ 0.9, for example, will provide confidence that the projected number of units screened will be underestimated less often (~10% of the time). Results: With F ≥ 0.9, the estimated number of attempted crossmatches ranges from 1.3 to 2.3 times as many as the number calculated by the current method. As a rule of thumb approximately 1.6 times the current estimated number provides a similar estimate (n ~ 1.6∙r/p). Conclusions: Waiting time underestimation will be reduced significantly by using the Negative Binomial Distribution solution and should be accompanied by improved customer satisfaction. [ABSTRACT FROM AUTHOR]

Phyllocnistis citrella Stainton, commonly known as the citrus leafminer, threatens global citrus production. This study focuses on elucidating the population dynamics and spatial distribution of P. citrella in lemon orchards located in Northern Portugal. From May to December, using delta traps with sexual pheromones, the levels of the adult population were monitored, and aggregation indices were calculated. Five distinct density peaks are observed, with the highest recorded in late July to early August. Spatial distribution consistently indicated an aggregated pattern. However, temporal variation in distribution was observed on specific dates. According to our results, it is suggested deploying two or three traps per hectare during peak density periods. This study significantly advances our understanding of P. citrella dynamics, emphasizing the need to consider spatial and temporal patterns for effective pest management. The outcomes underscore the importance of further exploration into factors influencing distribution patterns to refine control strategies. These insights are crucial for devising targeted and efficient measures to mitigate the impact of P. citrella on citrus orchards globally. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution and population fluctuations of insect pests facilitates the development of effective control strategies for a given crop. Conotrachelus psidii (Coleoptera: Curculionidae) is an important guava pest; however, no sampling plans are available for its management. Therefore, in this study, we aimed to (i) determine the spatial distribution pattern of C. psidii adults and (ii) develop a sequential sampling plan for pest control in guava crops. Evaluations began at 120 days after pruning and were performed weekly. To sample C. psidii adults, a white tarp was placed under the plants, covering the entire canopy. Plant branches were then shaken to dislodge the adult insects on the tarp, which were then identified and counted. Using the data obtained in each sampling, dispersion indices were determined and tests of adjustments to the theoretical Poisson and negative binomial frequency distributions were performed. The calculated dispersion indices revealed that the individuals in the studied populations exhibited aggregated dispersion. Notably, only four samples showed adjustment to the Poisson distribution, whereas 18 samples showed adjustment to the negative binomial distribution in the field, confirming the aggregated pattern of the population spatial arrangement. The sequential sampling plan developed for C. psidii revealed the maximum and minimum numbers of sample units expected for decision‐making as 45 and 15, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Acta Zoologica Lilloana is the property of Fundacion Miguel Lillo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Corn (Zea mays) is the most widely planted crop in the world. Dalbulus maidis (Hemiptera: Cicadellidae) is currently a primary corn pest. The starting point for the development of pest control decision-making systems is the determination of a conventional sampling plan. Therefore, this study aimed to determine a practical conventional sampling plan for D. maidis in corn crops. Insect density was evaluated in 28 commercial fields. Subsequently, D. maidis densities were sampled from fields ranging from 1 to 100 ha. Insect density conformed to a negative binomial distribution in 89.29% of the fields. The insect densities determined using the sampling plan had a low error rate (up to 15%). Sampling time and costs ranged from 2.06 to 39.45 min/ha and 0.09 to 1.81 USD/ha for fields of 1–100 ha, respectively. These results provide the first precise and representative conventional sampling plan for scouting D. maidis adults grown in corn fields. Therefore, the conventional sampling plan for D. maidis determined in this study is practical and can be incorporated into integrated pest management programs for corn crops owing to its representativeness, precision, speed, and low cost. [ABSTRACT FROM AUTHOR]

Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer‐valued network autoregressive model with a non‐random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi‐likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones. [ABSTRACT FROM AUTHOR]

Modeling multivariate integer-valued time series with appropriate methods is currently a popular research topic. In this paper, we propose a multivariate integer-valued autoregressive time series model based on a fixed network community structure. We use the negative binomial distribution as the conditional marginal distribution and a copula to construct the conditional joint distribution. The newly proposed model introduces the heterogeneity of nodes. Stability conditions are provided for both fixed and increasing dimensions. We estimate the parameters of the proposed model by maximizing the quasi-likelihood function with known and unknown community membership matrices, respectively. Corresponding asymptotic properties of parameter estimates are also provided. A simulation study is conducted to demonstrate the asymptotic behavior of the proposed model, and two real datasets are employed to compare the proposed model with other competitive models. • Model multivariate integer-valued time series with suitable methods is hot. • A model with network community structure is proposed, which has node heterogeneity. • Stability conditions for both fixed and increasing dimensions are provided. • Parameters are estimated with known and unknown community membership matrices. • Two real datasets are used for illustrating flexibility. [ABSTRACT FROM AUTHOR]

The control of parasitic nematode infection in sheep and other animals is threatened by the evolution of drug resistance in parasite populations. One recommendation to delay the onset of drug resistance is to estimate the flock mean faecal egg counts by sampling a subpopulation and to treat sheep only when egg counts are high. However, there is little research on the accuracy and precision of estimates of the flock mean obtained from samples. In silico sampling was used to quantify the influence of flock variation, sample size, flock size and mean egg count on the accuracy and precision of the estimated mean egg count. Commonly used and recommended sampling schemes gave alarmingly imprecise estimates of the true flock means. Simply providing a point estimate of the flock egg count can be seriously misleading. Therefore, quantiles were provided for the proportion of estimates in a plausible scenario that is likely to require treatment. It may be more informative to use these quantiles to predict the probability that the true flock mean is sufficiently high to consider treatment.

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler four-variate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums.

Background: Individualizing and optimizing treatment of relapsing-remitting multiple sclerosis patients is a challenging problem, which would benefit from a clinically valid decision support. Stühler et al. presented black box models for this aim which were developed and internally evaluated in a German registry but lacked external validation. Methods: In patients from the French OFSEP registry, we independently built and validated models predicting being free of relapse and free of confirmed disability progression (CDP), following the methodological roadmap and predictors reported by Stühler. Hierarchical Bayesian models were fit to predict the outcomes under 6 disease-modifying treatments given the individual disease course up to the moment of treatment change. Data was temporally split on 2017, and models were developed in patients treated earlier (n = 5517). Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the Bayesian ones. For both, we modelled the number of on-therapy relapses with a negative binomial distribution, and CDP occurrence with a binomial distribution. Results: The performance of our temporally-validated relapse model (MSE: 0.326, C-Index: 0.639) is potentially superior to that of Stühler's (MSE: 0.784, C-index: 0.608). Calibration plots revealed miscalibration. Our CDP model (MSE: 0.072, C-Index: 0.777) was also better than its counterpart (MSE: 0.131, C-index: 0.554). Results from non-Bayesian fixed-effects GLM models were similar to the Bayesian ones. Conclusions: The relapse and CDP models rebuilt and externally validated in independent data could compare and strengthen the credibility of the Stühler models. Their model-building strategy was replicable. [ABSTRACT FROM AUTHOR]

Background: Although administrative claims data have a high degree of completeness, not all medically attended Respiratory Syncytial Virus-associated lower respiratory tract infections (RSV-LRTIs) are tested or coded for their causative agent. We sought to determine the attribution of RSV to LRTI in claims data via modeling of temporal changes in LRTI rates against surveillance data. Methods: We estimated the weekly incidence of LRTI (inpatient, outpatient, and total) for children 0–4 years using 2011–2019 commercial insurance claims, stratified by HHS region, matched to the corresponding weekly NREVSS RSV and influenza positivity data for each region, and modelled against RSV, influenza positivity rates, and harmonic functions of time assuming negative binomial distribution. LRTI events attributable to RSV were estimated as predicted events from the full model minus predicted events with RSV positivity rate set to 0. Results: Approximately 42% of predicted RSV cases were coded in claims data. Across all regions, the percentage of LRTI attributable to RSV were 15–43%, 10–31%, and 10–31% of inpatient, outpatient, and combined settings, respectively. However, when compared to coded inpatient RSV-LRTI, 9 of 10 regions had improbable corrected inpatient LRTI estimates (predicted RSV/coded RSV ratio < 1). Sensitivity analysis based on separate models for PCR and antigen-based positivity showed similar results. Conclusions: Underestimation based on coding in claims data may be addressed by NREVSS-based adjustment of claims-based RSV incidence. However, where setting-specific positivity rates is unavailable, we recommend modeling across settings to mirror NREVSS's positivity rates which are similarly aggregated, to avoid inaccurate adjustments. [ABSTRACT FROM AUTHOR]

The control of parasitic nematode infection in sheep and other animals is threatened by the evolution of drug resistance in parasite populations. One recommendation to delay the onset of drug resistance is to estimate the flock mean faecal egg counts by sampling a subpopulation and to treat sheep only when egg counts are high. However, there is little research on the accuracy and precision of estimates of the flock mean obtained from samples. In silico sampling was used to quantify the influence of flock variation, sample size, flock size and mean egg count on the accuracy and precision of the estimated mean egg count. Commonly used and recommended sampling schemes gave alarmingly imprecise estimates of the true flock means. Simply providing a point estimate of the flock egg count can be seriously misleading. Therefore, quantiles were provided for the proportion of estimates in a plausible scenario that is likely to require treatment. It may be more informative to use these quantiles to predict the probability that the true flock mean is sufficiently high to consider treatment. [ABSTRACT FROM AUTHOR]

This paper describes various selected properties and features of negative binomial (NB) random variables, with special reference to NB2 (i.e., p = 2), and some generalizations to NBp (i.e., p ≥ 2), specifications. It presents new results (e.g., the NBp moment-generating function) with regard to the relationship between a sample mean and its accompanying variance, as well as spatial statistical/econometric numerical and empirical examples, whose parameter estimators are maximum likelihood or method of moment ones. Finally, it highlights the Moran eigenvector spatial filtering methodology within the context of generalized linear modeling, demonstrating it in terms of spatial negative binomial regression. Its overall conclusion is a bolstering of important findings the literature already reports with a newly recognized empirical example of an NB3 phenomenon. [ABSTRACT FROM AUTHOR]

Motivation Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. Results This study investigates a semisupervised clustering model called scTPC, which integrates the t riplet constraint, p airwise constraint, and c ross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. Availability and implementation scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780. [ABSTRACT FROM AUTHOR]

Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases. [ABSTRACT FROM AUTHOR]

In the correlated frailty models, it is believed that all the individuals in the population will eventually experience the event of interest. This may not always be the situation in reality. There may be a certain fraction of the population which is immune to the event and hence may not experience the event under study. For such individuals, frailty may not be active i.e., zero. Hence, frailty distribution should provide a positive mass at zero. In this article, a new correlated frailty model with compound negative binomial distribution as frailty distribution, is introduced to incorporate the non-susceptibility of individuals. The inferential problem is solved in a Bayesian framework using Markov Chain Monte Carlo methods. The proposed model is then applied to a real life data set. [ABSTRACT FROM AUTHOR]

We derive new upper bounds for the total variation distance between compound Poisson distributions as well as between a random sum and a compound Poisson distribution, and as a result we also obtain upper bounds for the total variation distance between compound Poisson distributions and a sum of independent random variables. These bounds are generalizations and refinements of some well-known bounds in the literature. We also derive upper bounds for the total variation distance between negative binomial distributions of order k and between the negative binomial distributions of order k and compound Poisson distributions. Upper bounds for the total variation distances between the number of success runs of length k in binary Markovian trials and its limiting distributions for several enumeration schemes, are also given. [ABSTRACT FROM AUTHOR]

Using a biorthogonal technique (Appell system), the foremost aim of this study is to develop and highlight specific aspects of a new polynomial sequence known as Fractional Krawtchouk Appell polynomials associated with the fractional Pascal probability distribution. Additionally, the connection that exists between brand-new polynomials and the orthogonal Bell polynomials has been investigated. [ABSTRACT FROM AUTHOR]

Background: Malaria elimination using current tools has stalled in many areas. Ivermectin (IVM) is a broad-antiparasitic drug and mosquitocide and has been proposed as a tool for accelerating progress towards malaria elimination. Under laboratory conditions, IVM has been shown to reduce the survival of adult Anopheles populations that have fed on IVM-treated mammals. Treating cattle with IVM has been proposed as an important contribution to malaria vector management, however, the impacts of IVM in this One Health use case have been untested in field trials in Southeast Asia. Methods: Through a randomized village-based trial, this study quantified the effect of IVM-treated cattle on anopheline populations in treated vs. untreated villages in Central Vietnam. Local zebu cattle in six rural villages were included in this study. In three villages, cattle were treated with IVM at established veterinary dosages, and in three additional villages cattle were left as untreated controls. For the main study outcome, the mosquito populations in all villages were sampled using cattle-baited traps for six nights before, and six nights after a 2-day treatment IVM-administration (intervention) period. Anopheline species were characterized using taxonomic keys. The impact of the intervention was analyzed using a difference-in-differences (DID) approach with generalized estimating equations (with negative binomial distribution and robust errors). This intervention was powered to detect a 50% reduction in total nightly Anopheles spp. vector catches from cattle-baited traps. Given the unusual diversity in anopheline populations, exploratory analyses examined taxon-level differences in the ecological population diversity. Results: Across the treated villages, 1,112 of 1,527 censused cows (73% overall; range 67% to 83%) were treated with IVM. In both control and treated villages, there was a 30% to 40% decrease in total anophelines captured in the post-intervention period as compared to the pre-intervention period. In the control villages, there were 1,873 captured pre-intervention and 1,079 captured during the post-intervention period. In the treated villages, there were 1,594 captured pre-intervention, and 1,101 captured during the post-intervention period. The difference in differences model analysis comparing total captures between arms was not statistically significant (p = 0.61). Secondary outcomes of vector population diversity found that in three villages (one control and two treatment) Brillouin's index increased, and in three villages (two control and one treatment) Brillouin's index decreased. When examining biodiversity by trapping-night, there were no clear trends in treated or untreated vector populations. Additionally, there were no clear trends when examining the components of biodiversity: richness and evenness. Conclusions: The ability of this study to quantify the impacts of IVM treatment was limited due to unexpectedly large spatiotemporal variability in trapping rates; an area-wide decrease in trapping counts across all six villages post-intervention; and potential spillover effects. However, this study provides important data to directly inform future studies in the GMS and beyond for IVM-based vector control. Author summary: Malaria incidence in Vietnam has substantially declined in the last decade, however, current prevention strategies may be insufficient to achieve national elimination goals. A proposed tool for use as a mosquitocide is zooprophylaxis-aided ivermectin-based elimination (i.e., treating cattle with ivermectin to kill feeding mosquitoes). Ivermectin (IVM) is an inexpensive helminthicide and is safe for use in mammals. In laboratory studies, mosquitoes feeding on IVM-treated cattle have increased mortality compared to controls. Presented here is a randomized village-based trial to determine whether IVM treatment can reduce the number of captured mosquitoes. Six villages in Central Vietnam were randomly assigned to either receive IVM treatment or remain a control. Mosquitoes in each village were captured for six trap nights, followed by treatment in three villages, followed by trapping for an additional six nights. A difference-in-differences model did not show any statistically significant differences in capture rates between the treated and control villages. Factors including the amount of circulating IVM in the cattle population, crossover of mosquito populations, and differential feeding habits may have affected captures. A total of 18 species were identified using dichotomous keys. In post hoc analyses, no clear trends were observed in the anopheline populations after IVM treatment, as measured by ecological diversity metrics. Future studies should include additional villages, greater control of cattle movements, and consider potential vector movements. [ABSTRACT FROM AUTHOR]

Descriptions of cloud ensembles in radiative convective equilibrium (RCE) rely mostly on the assumption that clouds are randomly distributed in space, a hypothesis that obviously fails in presence of strong organization. In this work, idealized RCE simulations at horizontal grid spacings ranging from 2 km to 125 m are analyzed, displaying a transition between complete randomness at coarse resolution to a high degree of cold pool driven organization at high resolution. The objective is then twofold: (a) characterizing the spatial cloud patterns focusing on correlation scales and association with covariates; (b) providing a statistical description of organized cloud ensembles to generalize existing theories based on complete randomness. It is demonstrated that organized cloud ensembles may be treated as heterogeneous point processes where individual clouds do not interact substantially with their neighbors. In other words, clouds may be considered as randomly distributed if we restrict the space to regions of high cloud density. In addition, it is shown that in highly organized situations local cloud counts may be modeled using negative binomial distributions, while cloud mass fluxes follow lognormal distributions. The total mass flux within regions of varying sizes can then be predicted by compounding the two aforementioned distributions. The total mass flux variability at scales ≲5 km is dominated by the heavy‐tails of the mass flux distributions, whereas at scales ≳5 km, it is entirely controlled by the spatial cloud count variability (the spatial cloud pattern). These results have important implications for the parameterization of organized convection in quasi‐equilibrium. Plain Language Summary: Convective clouds play a primary role in the Earth's energy budget and global hydrologic cycle. As such, it is of key importance that climate and weather forecast models are capable of accurately representing the impact of convection on the general state of the atmosphere. Unfortunately, convective clouds can only be explicitly represented in models using grid spacings ≲2 km. At coarser resolutions, moist convection is an unresolved process that needs to be represented using dedicated parameterizations. These latter typically aim at capturing the collective effect of many convective clouds, but ignore how these clouds are spatially distributed. The spatial cloud distribution is however presumed to have many important consequences on climate sensitivity, and on the intensity of convective events. This work is aimed at providing guidance as to how the spatial distribution of clouds should be parameterized in general atmospheric models. To this end, we rely on idealized high‐resolution numerical simulations of tropical convection in equilibrium with its large‐scale environment, and propose a thorough analysis of the spatial patterns observed in the simulated cloud ensembles. A statistical model capable of capturing the main properties of organized cloud populations across scales is finally presented. Key Points: Assuming only marginal cloud‐cloud interactions, organized cloud ensembles in radiative convective equilibrium can be modeled using heterogeneous spatial point processesAt scales smaller than 5 km, fluctuations in total mass flux are dominated by the variability in cloud mass fluxAt scales larger than 5 km, fluctuations in total mass flux are mostly controlled by the spatial variability in cloud count [ABSTRACT FROM AUTHOR]

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model's input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out. [ABSTRACT FROM AUTHOR]

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler fourvariate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums. [ABSTRACT FROM AUTHOR]

In addition, the authors find that salesperson analytical skills have an impact on salesperson performance as well as an interactive effect with salesperson effort on salesperson performance. Adopting the idea that salespeople should be evaluated based on salesperson institutionalization and by analyzing qualitative data collected from salespeople, authors argue that salesperson institutional performance comprises of three components: salesperson regulative performance, salesperson normative performance, and salesperson cognitive-cultural performance. The study employs a multiple-source date set of 294 salespeople to disentangle the degree to which complex service innovation selling is shaped by the interplay of salesperson service incentives and salesperson share of variable compensation. [Extracted from the article]

In this paper, we investigate a two-level supply chain consisting of a company which manufactures a set of products and distributes them via its central warehouse to a set of customers. The problem is modelled as a dynamic and stochastic inventory routing problem (DSIRP) that considers two flexible instruments of transshipment and substitution to mitigate shortages at the customer level. A new resolution approach, based on the hybridisation of mathematical modelling, Genetic Algorithm and Deep Reinforcement Learning is proposed to handle the combinatorial complexity of the problem at hand. Tested on the 150 most commonly used benchmark instances for single-vehicle-product DSIRP, results show that the proposed algorithm outperforms the current best results in the literature for medium and large instances. Moreover, 450 additional instances for multi-products DSIRP are generated. Different demand distributions are examined in these experiments, namely, Normal distribution, Poisson distribution for demand occurrence, combined with demands of constant size; Stuttering Poisson distribution and Negative Binomial distribution. In terms of managerial insights, results show the advantages of promoting inventory sharing and substitutions on the overall supply chain performance. [ABSTRACT FROM AUTHOR]

The shared frailty models are now popular for modelling heterogeneity in survival analysis. It assumes that the same frailty is shared by all individual members within the families. Also, it is believed that all the individuals in the population are susceptible to the event of interest and will eventually experience the event. This may not always be the situation in reality. There may be a certain fraction of the population which is non-susceptible for an event and hence may not experience the event under study. Non-susceptibility is modelled by frailty models with compound frailty distribution. Further, susceptibility may be different for different families. This can be attained by randomizing the parameter of frailty distribution. This paper incorporates both the things, non-susceptibility and different susceptibility for different families by considering compound negative binomial distribution with random probability of susceptibility as frailty distribution. The inferential problem is solved in a Bayesian framework using Markov Chain Monte Carlo methods. The proposed model is then applied to a real-life data set. [ABSTRACT FROM AUTHOR]

A new subclass T X q [ λ , A , B ] of analytic functions is introduced by making use of the q-derivative operator associated with the Pascal distribution. Certain properties of analytic functions in the subclass T X q [ λ , A , B ] are derived. Some known results are generalized. [ABSTRACT FROM AUTHOR]

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns. [ABSTRACT FROM AUTHOR]

There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Bayesian nonparametric (BNP) inference for the unseen-features problem: for fixed prior's parameters, they all lead to a Poisson posterior distribution for the number of unseen features, which depends on the sampling information only through the sample size. CRMs are thus not a flexible prior model for the unseen-features problem and, while the Poisson posterior distribution may be appealing for analytical tractability and ease of interpretability, its independence from the sampling information makes the BNP approach a questionable oversimplification, with posterior inferences being completely determined by the estimation of unknown prior's parameters. In this article, we introduce the stable-Beta scaled process (SB-SP) prior, and we show that it allows to enrich the posterior distribution of the number of unseen features arising under CRM priors, while maintaining its analytical tractability and interpretability. That is, the SB-SP prior leads to a negative Binomial posterior distribution, which depends on the sampling information through the sample size and the number of distinct features, with corresponding estimates being simple, linear in the sampling information and computationally efficient. We apply our BNP approach to synthetic data and to real cancer genomic data, showing that: (i) it outperforms the most popular parametric and nonparametric competitors in terms of estimation accuracy; (ii) it provides improved coverage for the estimation with respect to a BNP approach under CRM priors. for this article are available online. [ABSTRACT FROM AUTHOR]

A traffic accident is an event on the road that is unexpected and unintentional. Traffic accidents have an impact on the national economy as evidenced by the contribution of traffic accidents to economic losses in the form of a decrease in GDP by 2.9% - 3.1% or equivalent to USD 1.332 Billion - USD 1.465 Billion. So, to minimize accidents, an analysis is needed that is useful for anticipating accidents. This study intends to obtain the effect of the characteristics of intercity toll roads, as well as urban toll roads, on the accident rate. The stages conducted in this research are previous research studies, data collection, determining variables, and modeling. Based on the analysis that has been done in this study, a negative binomial distribution is used to determine the accident rate of inter-city toll roads and the Poisson distribution to determine the accident rate of toll roads within the city and the fatality rate of toll roads between cities and within cities. There are nine parameters used in this model, which can cover more things than similar studies that have been done before. [ABSTRACT FROM AUTHOR]

Background: Surveillance of malaria vectors is crucial for assessing the transmission risk and impact of control measures. Human landing catches (HLC) directly estimate the biting rates but raise ethical concerns due to the exposure of volunteers to mosquito-borne pathogens. A common alternative is the CDC-light trap, which is effective for catching host-seeking mosquitoes indoors but not outdoors. New, exposure-free methods are needed for sampling mosquitoes indoors and outdoors in ways that reflect their natural risk profiles. The aim of this study was therefore to evaluate the efficacy of the miniaturized double net trap (DN-Mini) for sampling host-seeking mosquitoes in south-eastern Tanzania, where malaria transmission is dominated by Anopheles funestus. Methods: Adult mosquitoes were collected from 222 randomly selected houses across three villages (74 per village) in Ulanga district, south-eastern Tanzania, using the DN-Mini traps, CDC-Light traps, and Prokopack aspirators. First, we compared CDC-light and DN-Mini traps for collecting indoor host-seeking mosquitoes, while Prokopack aspirators were used for indoor-resting mosquitoes. Second, we deployed the DN-Mini and Prokopack aspirators to collect host-seeking and resting mosquitoes indoors and outdoors. Generalized linear mixed models (GLMM) with a negative binomial distribution were used to compare the effectiveness of the traps for catching different mosquito species. Results: The DN-Mini was 1.53 times more efficient in collecting An. funestus indoors (RR = 1.53, 95% CI: 1.190–1.98) compared to the CDC-Light trap. However, for Anopheles arabiensis, the DN-Mini caught only 0.32 times as many mosquitoes indoors as the CDC-Light traps (RR = 0.32, 95% CI: 0.183–0.567). Both An. funestus and An. arabiensis were found to be more abundant indoors than outdoors when collected using the DN-Mini trap. Similarly, the Prokopack aspirator was greater indoors than outdoors for both An. funestus and An. arabiensis. Conclusion: The DN-Mini outperformed the CDC-light trap in sampling the dominant malaria vector, An. funestus species, but was less effective in capturing An. arabiensis, and for both vector species, the biting risk was greater indoors than outdoors when measured using the DN-Mini trap. These findings highlight the importance of selecting appropriate trapping methods based on mosquito species and behaviors. [ABSTRACT FROM AUTHOR]

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa. Author summary: The early stages of epidemics are characterised by dramatic variations in the number of new cases generated by each infectious individual, with some cases generating no new infections and some "superspreading" cases generating disproportionately large numbers of subsequent cases. In this study we introduce a mathematical model based on a two-step interpretation of infectious disease transmission: infectious individuals make a random number of contacts according to some fixed contact distribution and then infect their contacts with an infection probability which is unique to that specific infectious individual. This model has the advantage of generalizing more commonly used models of early epidemic dynamics, while allowing for policy analyses which assess the impact of measures which impact social contact behaviour and infectiousness across contacts separately. We find that while our model performs at least as well as pre-existing models in modelling individual-level capacity to generate new infections, the extra mathematical complexity our model introduces is not justified by commonly-used measures of parsimony. This suggests that our model could be applicable in specific policy settings but does not offer a substantial improvement over past approaches in a purely observational setting. [ABSTRACT FROM AUTHOR]

Impacts of long‐term fertilization and cultivation were evaluated on nematode communities associated with tall fescue turfgrass following 11 years of treatment applications. Fertilizer treatments of biosolid, synthetic, and plant‐based fertilizers and cultivation treatments of 0×, 1×, and 2× aerification passes were applied to randomized and replicated tall fescue plots at the University of Maryland Paint Branch Turfgrass facility in College Park, Maryland. Free‐living and plant‐parasitic nematodes were identified, enumerated, and categorized into functional groups. Nematode count data were compared using generalized linear mixed modeling with negative binomial distribution and two‐way ANOVA was used to compare nematode ecological indices. Biosolid treatments resulted in lower omnivore‐predator densities than plant‐based fertilizer treatments (p ≤.001) and significantly greater Hoplolaimus densities than plant‐based fertilizer plots (p ≤.05). Synthetic fertilizer applications resulted in the greatest Eucephalobus (p ≤.05) and total bacterivore densities (p ≤.001) of all fertilizer treatments. Plant‐based fertilizer‐treated plots had the largest Maturity Index cp 2‐5 and Structure Index (p ≤.05). Cultivation of 1× resulted in fewer total bacterivore densities than 2× (p ≤.01) while omnivore‐predator densities were greater in 1× than 0× (p ≤.001). Plant health, as measured by NDVI, was lowest in biosolid‐treated turfgrass (p ≤.05). These findings suggest that long‐term turfgrass management practices can have variable impacts on nematode abundance and community structure in tall fescue and provide insights into ecological impacts of turfgrass management practices. [ABSTRACT FROM AUTHOR]

Characterizing the sleep-wake cycle in adolescents is an important prerequisite to better understand the association of abnormal sleep patterns with subsequent clinical and behavioral outcomes. The aim of this research was to develop hidden Markov models (HMM) that incorporate both objective (actigraphy) and subjective (sleep log) measures to estimate the sleep-wake cycle using data from the NEXT longitudinal study, a large population-based cohort study. The model was estimated with a negative binomial distribution for the activity counts (1-minute epochs) to account for overdispersion relative to a Poisson process. Furthermore, self-reported measures were dichotomized (for each one-minute interval) and subject to misclassification. We assumed that the unobserved sleep-wake cycle follows a two-state Markov chain with transitional probabilities varying according to a circadian rhythm. Maximum-likelihood estimation using a backward–forward algorithm was applied to fit the longitudinal data on a subject by subject basis. The algorithm was used to reconstruct the sleep-wake cycle from sequences of self-reported sleep and activity data. Furthermore, we conduct simulations to examine the properties of this approach under different observational patterns including both complete and partially observed measurements on each individual. [ABSTRACT FROM AUTHOR]

We present a numeric search algorithm to determine confidence intervals for the parameters of the negative binomial distribution that are substantially more precise (shorter) than those of any existing method. We derive confidence intervals for the mean μ for any specified number of successes k by means of a conditional minimal cardinality approach that efficiently accommodates the relationship of the variability of the negative binomial distribution to its mean. Confidence intervals for the success parameter p are easily obtained from the inverse relationship between μ and p. [ABSTRACT FROM AUTHOR]

The latent growth model (LGM) is a popular tool in the social and behavioral sciences to study development processes of continuous and discrete outcome variables. A special case are frequency measurements of behaviors or events, such as doctor visits per month or crimes committed per year. Probability distributions for such outcomes include the Poisson or negative binomial distribution and their zero-inflated extensions to account for excess zero counts. This article demonstrates how to specify, evaluate, and interpret LGMs for count outcomes using the Mplus program in the structural equation modeling framework. The foundations of LGMs for count outcomes are discussed and illustrated using empirical count data on self-reported criminal offenses of adolescents (N = 1,664; age 15–18). Annotated syntax and output are presented for all model variants. A negative binomial LGM is shown to best fit the crime growth process, outperforming Poisson, zero-inflated, and hurdle LGMs. [ABSTRACT FROM AUTHOR]